Assessing the impact of health-care access on the severity of low back pain by country: a case study within the GBD framework

Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is key for policy making. Low back pain is the leading cause of disability in terms of years lived with disability (YLDs). Due to sparse data, a current limitation of GDB is that a uniform severity distribution is presumed based on 12-Item Short Form Health Survey scores derived from US Medical Expenditure Panel Surveys (MEPS). We present a novel approach to estimate the effect of exposure to health interventions on the severity of low back pain by country and over time. Methods We extracted treatment effects for ten low back pain interventions from the Cochrane Database, combining these with coverage data from the MEPS to estimate the hypothetical severity in the absence of treatment in the USA. Severity across countries was then graded using the Health Access and Quality Index, allowing estimates of averted and avoidable burden under various treatment scenarios. Findings We included 210 trials from 36 Cochrane systematic reviews in the network analysis. The pooled effect sizes (measured as a standardised mean difference) for the most effective intervention classes were –0·460 (95% uncertainty interval –0·606 to –0·309) for a combination of psychological and physical interventions and –0·366 (–0·525 to –0·207) for surgery. Globally, access to treatment averted an estimated 17·6% (14·8 to 23·8) of the low back pain burden in 2020. If all countries had provided access to treatment at a level estimated for Iceland with the highest Health Access and Quality Index score, an extra 9·1% (6·4 to 11·2) of the burden of low back pain could be avoided. Even with full coverage of optimal treatment, a large proportion (65·9% [56·9 to 70·4]) of the low back pain burden is unavoidable. Interpretation This methodology fills an important shortcoming in the GBD by accounting for low back pain severity variations over time and between countries. Assumptions of unequal treatment access increased YLD estimates in resource-poor settings, with a modest decrease in countries with higher Health Access and Quality Index scores. Nonetheless, the large proportion of unavoidable burden indicates poor intervention efficacy. This method, applicable to other GBD conditions, provides policy makers with insights into health gains from improved treatment and underscores the importance of investing in research for new interventions. Funding Bill and Melinda Gates Foundation and Queensland Health.


Table of Contents
Provide a conceptual overview of the data analysis method.A diagram may be helpful.Figure S1 (page 4)

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Provide a detailed description of all steps of the analysis, including mathematical formulae.This description should cover, as relevant, data cleaning, data pre-processing, data adjustments and weighting of data sources, and mathematical or statistical model(s).

11
Describe how candidate models were evaluated and how the final model(s) were selected.Methods (pages 4-7) and appendix (pages 5-

6) 12
Provide the results of an evaluation of model performance, if done, as well as the results of any relevant sensitivity analysis.Provide published estimates in a file format from which data can be efficiently extracted.Appendix (Table S7, Table S8) 16 Report a quantitative measure of the uncertainty of the estimates (e.g.uncertainty intervals).
All estimates are provided with 95% uncertainty intervals.17 For GBD 2013, a 12-item Short Form Health Survey with questions asking about hypothetical individuals experiencing specific health states was conducted among 2338 respondents providing SF-12 scores for 62 health states 1 .Each respondent completed Sf-12 forms with up to 50 randomly selected health states.These health states were randomly selected from a section with 62 health states in GBD that reflected a broad range of spectrum of health state DWs from least to most severe 1 .This map derived from this study was used in all subsequent GBD studies.
In the original method, due to the high heterogeneity among SF-12 scores, SF-12 scores with more than two absolute deviations from the median, approximately 18% of the observations, were removed from the analysis.A loess model was conducted on the observed SF-12 score for each health state and its average disability weight to obtain the estimated SF-12 score for each health state.The model was adjusted further with a mixed effect model.
The original method is straightforward but also comes with some deficiencies.The exclusion method was too crude and about 1/5 of the observations were removed.With a more refined method, more observations could be retained in the analysis.Secondly, no transformation was used before the loess model so that the mean disability weight could be lower than 0 or greater than 1, which does not align with the current method.Moreover, the loess model does not include uncertainty, and the original method used average disability weight in the model instead of individual response.
The new method addresses all the issues listed above.First, a logit transformation was used to solve the issues that the disability weight is beyond the range from [0,1].We conducted a meta-regression using SF-12 scores and the transformed disability weight via a quadratic spine using MR-BRT (Meta-Regression: Bayesian, Regularized, Trimmed) 2 .For the first improvement, instead of using median, we used SF-12 scores ± 1.64* Standard deviation from the mean representing the top and bottom 5% were removed.About 9% of the observation were excluded in the end.We started using SF-12 scores on disability weights via quadratic spline, but the spline failed because a substantial number of observations for health states had a disability weight of less than 0.5 (Figure S2).We modified the first model by using aggregated data instead of using data with an individual health state, which improved from the first model across the entire spectrum (Figure S3).However, the aggregated SF-12 scores would ignore the heterogeneity of the individual observations.Therefore, we finalized our model with individual level SF-12 scores and used the intercept of the spline coefficients from the aggregate model as a prior method, and included gamma in the prediction to obtain uncertainty.The final model covers a broad spectrum of disability weight with uncertainty (Figure S4).

MR-BRT
MR-BRT(meta-regression-Bayesian, regularized, trimmed) was initially developed in-house at IHME and this tool has been applied widely in recent GBD studies.It is a customized meta-regression tool and the model is a trimmed constrained mixed-effect model that solves common linear and nonlinear mixed effects models using maximum likelihood estimation 2 .
Since we use MR-BRT for the meta analysis on the Cochrane reviews, to account for study heterogeneity, a gamma term( with a precision of 0.001) and random effects were used.The Gamma term is the coefficient of SMD.Since the precision is 0.001, this would suggest the standard error of the gamma coefficient is very low, indicating high confidence in the stability and reliability of this estimate within the meta regression model.We used the 10% trimming option, and we removed the top 10% outliers in the meta-regression analysis to ensure heterogeneity.The exclusion criteria were: 1) treatments for conditions other than LBP 2) studies that did not explicitly review healthcare interventions or treatments, for example, review of diagnostic criteria, and prescription adherence 3) studies focusing on subpopulations like pregnant women and children 4) reviews that only presented a qualitative analysis of the treatment effects 5) reviews that did not measure functional disability or status.In addition, we included reviews that measure the treatment effect of LBP vs. the effect in the reference group on a measure of functional status and disability.

MEPS data
As for GBD 2021, Medical Expenditure Panel Survey(MEPS) was used to inform the severity distribution split for low back pain in GBD.MEPS is a national health survey, that began in 1996, and collected data from families and individuals, their medical providers, and employers across the USA 3 .MEPS collects information on participants' health system encounters, including self-reported underlying diseases, health service utilization, and healthcare expenditures.Panels are two-year-long in five rounds, with 30,000 to 35,000 individuals in each panel.MEP data started from 2000 to 2012 was used in the analysis because composite SF-12 scores began to be collected in 2000.
We attempted to include more data; however, the occupational therapy, Physical intervention, and psychotherapyrelated questions were removed from surveys after 2012.

Utilization of treatment
Individual utilization of pharmaceutical intervention is defined as the use of at least one medication in that intervention class in the therapeutic class.Utilization of surgery for treating LBP was assessed using MEPS hospital visits data including office-based medical provider visits, emergency room visits, outpatient department visits and hospital inpatient stays datasets.
Treatment utilization estimates were acquired from the MEPS household component event files which contain responses related to medical events through self-reported surveys 4 .The event files contain different types of clinical visit data: dental visits, office-based medical provider visits, inpatient hospitalization, emergency room visits, outpatient visits, and home health events.For the utilization estimation of healthcare interventions for LBP, the data was limited to the events that were associated with LBP.The classes in MEPS were aligned with the classes used in treatment effect estimation.The drug data was mapped to normalized drug names using the Multum Lexicon labels from the Cerner Multum.Inc. drug database.Individual utilization of surgical intervention was defined as if a patient received anesthesia and a surgical procedure due to LBP in at least one hospital/office visit.
Utilization of behavioral cognitive therapies and physical interventions were assessed using the MEPS office-based medical provider visits, outpatient visits, and home health files.These data contain information indicating if an individual received Physical intervention, occupational therapy, or psychotherapy during the visits.Moreover, the ICD-9 procedure codes associated with LBP were also retrieved as a complementary of the hospital visits data (first two characters of ICD-9 procedure=93 psychotherapy, =94 physical intervention).The utilization of combined therapies was defined as utilizing multiple treatments simultaneously for treating LBP.MEPS data spans from 2000 to 2012 and the panels after 2012 were not included because the treatment-related questions were removed from the survey.
The most frequently used treatment in LBP is non-opioid NSAID across all panels, followed by physical treatments.
Surgery is often used in more severe LBP (Figure S6   12  , = 12  +   *  12 (equation 1) For the full utilization optimal treatment scenario(FUOT), using 12  , calculated from equation 2, 12 , is calculated by multiplying the SF-12 SD with the FUOT intervention effect size and subtracting from the SF-12 score of the correspondent treatment to get the SF-12 for the FUOT scenario at patient-level (Equation 3).
12 , = 12  , −   *  12 (equation 2) Then both 12  , and 12 , was mapped back to disability weight and followed by binning them into severity specific-sequelae as asymptomatic, mild, moderate, severe, and most severe.The proportion and the disability weight of each severity-specific sequela for each scenario were calculated respectively (Figure S8).
The healthcare access quality index 5 (HAQI) was used as a proxy for treatment access due to the lack of data on treatment coverage for low back pain.The HAQI is informed by the mortality rate of 32 causes of death which should not, or rarely, occur in the presence of effective care.The index spans between 0 (worst) and 100 (best) representing the 1 st and 99 th percentile observed since 1990 and every location-year has a corresponding HAQI score.

𝐴𝐴𝑆𝑆 =
−ℎ,   −ℎ,  An adjustment factor(AF) to sequela-weighted disability weight was calculated using MEPS data as the ratio of disability weight with current treatment over disability weight with no treatment.The HAQI value is assumed to be 0 for the no-treatment scenario, where an individual does not have any access to LBP interventions and the adjustment factor equals 1 for the US in 2007, while the HAQI was 81.0 [79.5 to 82.5].Assuming the association between HAQI and adjustment factor is linear, it was estimated that with 1 unit increase in HAQI would result in a 0.0037 [0.0037, 0.0037] unit increase in the adjustment factor (Figure S7).Using the 2020 HAQI, Iceland had the highest disease-specific accessibility 94.19[93.1,95.1] globally, which is used in the full utilization optimal scenario.Central African Republic had the lowest HAQI 14. 0[12.5, 15.5].The Sequela-weighted disability weights were adjusted in the following way: Where   represents the average disability weight from 2010 for low back pain, and adjusted by deviding the   which is the adjustment factor of HAQI.   is the proportion of low back pain cases in severity  observed from USA MEPS data 2010, where s represents severity (mild, moderate, severe, most severe), and the HAQI was from the USA in 2010 (the year that corresponds to the midpoint of data collection years for MEPS).
Then the proportion for each health state  varying by HAQI using MEPS 2010 is calculated as: The proportion in the asymptomatic state was calculated as the residual proportion subtracting from 1: Each experiment was repeated 1000 times to add uncertainty to the calculation.There are some draws where the proportion of asymptomatic cases is negative.To resolve this issue, we set the proportion of asymptomatic cases as 0 and repeated the similar method in equation 5. Instead of calculating the mild sequela residual proportion, we calculated the moderate sequela residual proportion.Followed by calculating the difference between the resulting disability weight when using mild sequela residual proportion as the residual proportion and the HAQI-specific sequela-weighted disability weight.Using this method, we ensure the sum of the proportion sequela is equal to one.
(equation 6) We re-iterate the process until all the severity proportions are non-negative.The correction for the most severe category is described as following as an example: The proportion in the severe sequela would be the new residual sequela proportion:
Figure S2 Regression of individual-level SF-12 scores on disability weights via quadratic spline in MR-BRT.

Figure S3 :
Figure S3: Regression of aggregate-level SF-12 scores on disability weights via quadratic spline in MR-BRT

FigureSection 2 :
Figure S4 Regression of individual-level SF-12 scores on disability weights via quadratic spline in MR-BRT using spline priors from the aggregate model.

Figure S5 .
Figure S5.PRISMA diagram for the search of Cochrane library for reviews of LBP treatment effects.
Top).In the utilization of combined therapy, the most frequently used are cognitive behavioral therapies + physical interventions (Figure S6 Bottom).

Figure S6 .Section 4 :
Figure S6.Utilization plots using MEPS data by MEPS panel.Shaded areas represent a 95% confidence interval.Top: Individual treatment; Bottom: combined treatment.

Figure S7 .
Figure S7.Adjustment factor to 2007 US Medical Expenditure Panel Survey sequela-weighted disability weight varying by healthcare access quality index

Figure S8 .
Figure S8.MEPS disability weight distributions by the scenario in the MEPS sample for LBP with and without leg involvement.Density plots of three scenarios: 1) with no treatment 2) with current treatment 3) full utilization optimal treatment.Black lines represent the mean of the distribution.

Figure S9 .
Figure S9.Distribution of disability weights among LBP cases for three scenarios (Upper Fig: LBP without leg pain; Lower Fig: LBP with leg pain.Dotted lines represent the cutoffs) in MEPS.
Describe methods for calculating uncertainty of the estimates.State which sources of uncertainty were, and were not, accounted for in the uncertainty analysis. N/A13

Table S5 . The current definition for the eight low back pain health states and their latest disability weight with 95% uncertainty interval. Table S6. Results compared against WHO non-surgical guidelines.
YesSection 3:

Table S7 : Predicted severity proportions for low back pain for 2020 by country, region and super-region 284 using the data with current treatment Table S8. PRISMA Checklist Study
selection 16a Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g.confidence/credible interval), ideally using structured tables or plots.For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.Results, Table1,2, Figure1, pages 8-1220b Present results of all statistical syntheses conducted.If meta-analysis was done, present for each the summary estimate and its precision (e.g.confidence/credible interval) and measures of statistical heterogeneity.If comparing groups, describe the direction of the effect.Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results.Results, Table1, page 10Reporting biases 21 Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.Provide registration information for the review, including register name and registration number, or state that the review was not registered.NA, secondary analysis 24b Indicate where the review protocol can be accessed, or state that a protocol was not prepared.The data and the code are available on github 24c Describe and explain any amendments to information provided at registration or in the protocol.Secondary analysis, no registration Support 25 Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.